某涡扇发动机最小油耗模式性能优化算法研究

针对发动机安全性、减少油耗、降低热应力问题,研究某型涡扇发动机非线性数学模型,在满足发动机各部件物理约束条件和推力相等的条件下,采用遗传算法就巡航状态下耗油率最低,对性能指标进行寻优,寻优过程用GAlib类库的遗传算法和涡扇发动机非线性数学模型结合编程实现.在地面及空中巡航状态下分别进行仿真,在推力相等的条件下,地面巡航状态的耗油率在优化后比优化前降低了13.8%,空中巡航状态的耗油率在优化后比优化前降低了9.45%.研究表明:遗传算法适用于像涡扇发动机巡航状态性能寻优这样大规模、高度非线性及无解析表达式的性能优化.

Performance Optimization of Turbofan Engine Minimizing Fuel Consumption Mode

Minimizing fuel consumption mode and the physical onnstraints of a certain turbofan engine were ana-lyzed, and Genetic Algorithm (GA) was applied to improve the engine performance in terms of the performance see-king control (PSC) mode above. The optimization process was realized by the combination of turbofan engine nonlin-ear component level model and Galib, a C + +library developed by MIT. Specific fuel consumption on ground and in-flight were greatly improved by the performance optimization: compared to the non-optimized model, specific fuel consumption decreased 13. 8% on ground and 9.45% in-flight. The study shows that GA, as an effective glob-al parallel optimization search tool, can be effectively applied to the performance optimization of turbofan engine cruise mode that is large scale, highly nonlinear and without explicitl expression.